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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 25 February 2027 | Viewed by 9031

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Editor

Prof. Dr. Codruta Orniana Ancuti

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Guest Editor

Faculty of Electronics, Telecommunications and Information Technologies, Polytechnic University Timisoara, 300223 Timisoara, Romania
Interests: computer vision; artificial intelligence; image processing

Special Issue Information

Dear Colleagues,

Recent advances in sensing technologies and imaging systems have significantly enhanced the capabilities of computer vision applications across various domains, including robotics, healthcare, environmental monitoring, and smart cities. This Special Issue aims to bring together cutting-edge research that explores novel sensing modalities, image acquisition techniques, and intelligent vision algorithms designed to interpret complex scenes and dynamic environments. Emphasis is placed on both theoretical innovations and practical implementations that push the boundaries of sensing-driven computer vision.

Prof. Dr. Codruta Orniana Ancuti
Guest Editor

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Keywords

computer vision
imaging sensors
multimodal sensing
AI in image understanding
remote sensing
sensor fusion

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Published Papers (7 papers)

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Research

16 pages, 2100 KB

Open AccessArticle

An Optimized Image-Processing Algorithm for Semi-Automated Measurement of Attached Cavities in High-Speed Flow Visualization

by Darya V. Litvinova, Ulyana S. Zubairova and Aleksandra Yu. Kravtsova

Sensors 2026, 26(16), 5166; https://doi.org/10.3390/s26165166 - 14 Aug 2026

Viewed by 415

Abstract

High-speed flow visualization provides imaging data containing quantitative information about cavitating-flow dynamics. Accurate determination of attached-cavity length is essential for characterizing cavitation regimes and validating mathematical models. In this study, an advanced image-processing algorithm for semi-automated analysis of cavitation patterns near hydrofoils is proposed. High-speed visualization data obtained for cavitating flow around a NACA0012 hydrofoil in a slit channel were used as input to the algorithm. The developed approach includes hydrofoil suppression, Otsu-based image binarization with threshold correction, filtering, and automated cavity-boundary detection. The initial search region for the cavity inception point is specified manually, whereas subsequent boundary tracking and cavity-length calculation are performed automatically. A dimensionless threshold correction coefficient was introduced to improve cavity identification, and its optimal range was determined. Additional geometric criteria were proposed to identify the cavity inception and closure locations and to separate attached cavities from detached vapor structures. The analysis showed that the optimal range of the threshold correction coefficient was

0.5

t h

0.7

, while a geometric connectivity criterion based on a distance of 7 px between neighboring boundary pixels provided stable detection of the cavity closure location. The developed algorithm enables the determination of both instantaneous and time-averaged attached-cavity lengths, with a total estimated uncertainty not exceeding 3.5%. Comparison with previously published experimental and analytical data demonstrated good agreement and supported the reliability of the proposed approach. The method provides an explainable and training-free computer-vision pipeline that can potentially be adapted to other bluff-body geometries under comparable imaging and contrast conditions. It can also support automated annotation and the generation of reference datasets for the development and validation of future machine-learning methods for cavitation-flow analysis. Full article

(This article belongs to the Special Issue Sensing and Imaging in Computer Vision)

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39 pages, 4062 KB

Open AccessArticle

Understanding the Performance of Deep Computer Vision Models: A Symbolic Regression Approach to Accuracy and Latency Prediction

by Divyesh Rameshbhai Dhanani, Faraz Kayani, Saif U Din, Alice Arslanian, Dmitry Ignatov and Radu Timofte

Sensors 2026, 26(13), 4093; https://doi.org/10.3390/s26134093 - 27 Jun 2026

Viewed by 584

Abstract

Deploying deep vision models on edge hardware requires understanding how architecture and training hyperparameters jointly determine accuracy and inference latency, yet these relationships remain poorly characterized in a systematic, data-driven manner. This paper presents a two-stage statistical framework providing interpretable, closed-form insights into both. In the first stage, we apply distance correlation (dCor) and the maximal information coefficient (MIC) across seven image-classification datasets, revealing that batch size and total layer count are the strongest universal accuracy predictors (mean dCor: 0.228 and 0.174), while learning rate achieves the highest MIC (0.226), reflecting a non-monotonic relationship with accuracy. In the second stage, PySR symbolic regression (representing, to our knowledge, the first application to cross-dataset vision model accuracy prediction) derives compact, interpretable formulas. Dataset-specific models achieve

R^{2}

from 0.20 to 0.45; a universal model achieves a mean leave-one-dataset-out

R^{2}

of 0.23, remaining strictly positive on all held-out datasets, whereas ordinary linear regression collapses to

R^{2} = - 0.71

. We further derive device-specific inference latency formulas for CPU, GPU, and NPU, outperforming classical baselines by

6.7 \times

–

14.8 \times

R^{2}

and confirming fundamental device heterogeneity. Together, these results offer interpretable surrogate models for screening deep vision architectures under accuracy and latency constraints in edge deployment. Full article

(This article belongs to the Special Issue Sensing and Imaging in Computer Vision)

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25 pages, 4947 KB

Open AccessArticle

QG-WRN: A Quantum-Enhanced Graph Convolutional Wide Residual Network for ASD Diagnosis via Neuroimaging Sensing Technology

by Nanting Huang, Xiaoyu Li, Xin Yang, Li Xie, Guowu Yang and Liujiang Zhou

Sensors 2026, 26(13), 3997; https://doi.org/10.3390/s26133997 - 24 Jun 2026

Viewed by 370

Abstract

The pathological mechanism of autism spectrum disorder (ASD) exhibits dual heterogeneity: abnormal local energy metabolism and brain-wide high-order topological failure. To synergistically characterize these complex signals captured by advanced neuroimaging sensors, we propose the Quantum-Enhanced Graph Convolutional Wide Residual Network (QG-WRN), a modality-specific, decoupled parallel dual-stream architecture. In the classical branch, to accurately capture the spatial distribution of local metabolic abnormalities, we employ a wide residual network (WRN) to extract amplitude of low-frequency fluctuation (ALFF) features, leveraging its expanded feature channels to effectively mine regional neurodynamic properties. Furthermore, to overcome the representational bottlenecks of classical linear operators in parsing hidden, long-range network connections, we introduce quantum computing, exploiting its exponentially expansive state space and intrinsic low-parameter regularization mechanism. Guided by these properties, the quantum branch utilizes a variational quantum graph convolutional (QGCN) module—featuring a trainable circular encoding strategy and a hardware-efficient 4-qubit configuration—with a 2-layer nested message passing structure to process the functional connectivity (FC) matrix, harnessing quantum interference in Hilbert space to parse complex topology while effectively mitigating overfitting on small-sample medical data. A unified training scheme achieves full-dimensional fusion of node activity and topology. Achieving 68.49% accuracy, our method outperforms 10 classic and recent new baselines, providing a powerful computational intelligence tool for sensor-based ASD clinical diagnosis. Furthermore, interpretability analysis successfully maps core disease hubs to standard AAL116 atlas coordinates, providing a powerful tool for computationally aided ASD diagnosis. Full article

(This article belongs to the Special Issue Sensing and Imaging in Computer Vision)

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28 pages, 10414 KB

Open AccessArticle

MBFTFuse: A Triple-Path Adversarial Network Based on Modality Balancing and Feature-Tracing Compensation for Infrared and Visible Image Fusion

by Mingxi Chen, Bingting Zha, Rui Yang, Yuran Tan, Shaojie Ma and Zhen Zheng

Sensors 2026, 26(7), 2109; https://doi.org/10.3390/s26072109 - 28 Mar 2026

Viewed by 561

Abstract

Infrared and visible image fusion aims to integrate complementary information from heterogeneous images captured by different optical sensors based on distinct imaging principles; however, existing methods often exhibit modality bias, leading to weakened targets or the loss of crucial texture details. To address this, we propose MBFTFuse, an adversarial fusion network based on modality balancing and feature tracing, which consists of a triple-path generator and dual discriminators. The architecture employs a generator with a triple-path structure: a central modality-balancing path for deep feature fusion and dual edge feature-tracing paths for modality-specific enhancement. Specifically, a multi-cognitive modality-balancing module is introduced to achieve feature weight equilibrium, while a Feature-Tracing Attention Module self-enhances single-modality features to compensate for information loss in the fusion results. Furthermore, a pixel loss based on intensity histograms is designed to optimize inter-modal balance at the pixel level. Comparative experiments against nine state-of-the-art methods across three public datasets demonstrate that MBFTFuse effectively highlights infrared targets while preserving intricate visible textures. The superior performance of this method in both quantitative metrics and downstream object detection tasks contributes to extending the boundaries of sensor-driven computer vision technologies. Full article

(This article belongs to the Special Issue Sensing and Imaging in Computer Vision)

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14 pages, 21956 KB

Open AccessArticle

Evaluating Image Quality Metrics as Loss Functions for Image Dehazing

by Rareș Dobre-Baron, Adrian Savu-Jivanov and Cosmin Ancuți

Sensors 2025, 25(15), 4755; https://doi.org/10.3390/s25154755 - 1 Aug 2025

Cited by 2 | Viewed by 2081

Abstract

The difficulty and manual nature of procuring human evaluators for ranking the quality of images affected by various types of degradations, and of those cleaned up by developed algorithms, has lead to the widespread adoption of automated metrics, like the Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index Metric (SSIM). However, disparities between rankings given by these metrics and those given by human evaluators have encouraged the development of improved image quality assessment (IQA) metrics that are a better fit for this purpose. These methods have been previously used solely for quality assessments and not as objectives in the training of neural networks for high-level vision tasks, despite the potential improvements that may come about by directly optimizing for desired metrics. This paper examines the adequacy of ten recent IQA metrics, compared with standard loss functions, within two trained dehazing neural networks, with observed broad improvement in their performance. Full article

(This article belongs to the Special Issue Sensing and Imaging in Computer Vision)

18 pages, 4066 KB

Open AccessArticle

Video Segmentation of Wire + Arc Additive Manufacturing (WAAM) Using Visual Large Model

by Shuo Feng, James Wainwright, Chong Wang, Jun Wang, Goncalo Rodrigues Pardal, Jian Qin, Yi Yin, Shakirudeen Lasisi, Jialuo Ding and Stewart Williams

Sensors 2025, 25(14), 4346; https://doi.org/10.3390/s25144346 - 11 Jul 2025

Cited by 4 | Viewed by 2478

Abstract

Process control and quality assurance of wire + arc additive manufacturing (WAAM) and automated welding rely heavily on in-process monitoring videos to quantify variables such as melt pool geometry, location and size of droplet transfer, arc characteristics, etc. To enable feedback control based upon this information, an automatic and robust segmentation method for monitoring of videos and images is required. However, video segmentation in WAAM and welding is challenging due to constantly fluctuating arc brightness, which varies with deposition and welding configurations. Additionally, conventional computer vision algorithms based on greyscale value and gradient lack flexibility and robustness in this scenario. Deep learning offers a promising approach to WAAM video segmentation; however, the prohibitive time and cost associated with creating a well-labelled, suitably sized dataset have hindered its widespread adoption. The emergence of large computer vision models, however, has provided new solutions. In this study a semi-automatic annotation tool for WAAM videos was developed based upon the computer vision foundation model SAM and the video object tracking model XMem. The tool can enable annotation of the video frames hundreds of times faster than traditional manual annotation methods, thus making it possible to achieve rapid quantitative analysis of WAAM and welding videos with minimal user intervention. To demonstrate the effectiveness of the tool, three cases are demonstrated: online wire position closed-loop control, droplet transfer behaviour analysis, and assembling a dataset for dedicated deep learning segmentation models. This work provides a broader perspective on how to exploit large models in WAAM and weld deposits. Full article

(This article belongs to the Special Issue Sensing and Imaging in Computer Vision)

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24 pages, 11104 KB

Open AccessArticle

HGCS-Det: A Deep Learning-Based Solution for Localizing and Recognizing Household Garbage in Complex Scenarios

by Houkui Zhou, Chang Chen, Zhongyi Xia, Qifeng Ding, Qinqin Liao, Qun Wang, Huimin Yu, Haoji Hu, Guangqun Zhang, Junguo Hu and Tao He

Sensors 2025, 25(12), 3726; https://doi.org/10.3390/s25123726 - 14 Jun 2025

Cited by 13 | Viewed by 1473

Abstract

With the rise of deep learning technology, intelligent garbage detection provides a new idea for garbage classification management. However, due to the interference of complex environments, coupled with the influence of the irregular features of garbage, garbage detection in complex scenarios still faces significant challenges. Moreover, some of the existing research suffer from shortcomings in either their precision or real-time performance, particularly when applied to complex garbage detection scenarios. Therefore, this paper proposes a model based on YOLOv8, namely HGCS-Det, for detecting garbage in complex scenarios. The HGCS-Det model is designed as follows: Firstly, the normalization attention module is introduced to calibrate the model’s attention to targets and to suppress the environmental noise interference information. Additionally, to weigh the attention-feature contributions, an Attention Feature Fusion module is employed to complement the attention weights of each channel. Subsequently, an Instance Boundary Reinforcement module is established to capture the fine-grained features of garbage by combining strong gradient information with semantic information. Finally, the Slide Loss function is applied to dynamically weight hard samples arising from the complex detection environments to improve the recognition accuracy of hard samples. With only a slight increase in parameters (3.02M), HGCS-Det achieves a 93.6% mean average precision (mAP) and 86 FPS on the public HGI30 dataset, which is a 3.33% higher mAP value than from YOLOv12, and outperforms the state-of-the-art (SOTA) methods in both efficiency and applicability. Notably, HGCS-Det maintains a lightweight architecture while enhancing the detection accuracy, enabling real-time performance even in resource-constrained environments. These characteristics significantly improve its practical applicability, making the model well suited for deployment in embedded devices and real-world garbage classification systems. This method can serve as a valuable technical reference for the engineering application of garbage classification. Full article

(This article belongs to the Special Issue Sensing and Imaging in Computer Vision)

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